The present invention relates to a graph plotting device and a graph plotting method, and more specifically relates to a device and a method for plotting graph representing correlation between one variate and another variate.
The present invention also relates to a yield analyzing method and a yield improvement support system which performs yield analysis while plotting a graph by executing such a graph plotting method.
The present invention also relates to a computer-readable program for executing those methods.
The present invention also relates to a computer-readable recording medium storing those methods.
In the fields such as the semiconductor product manufacturing field, a large amount of data of many kinds, such as processing shapes (line width, oxide film thicknesses, etc.) and processing lapse time in product processing, are collected to perform process-data analysis, one of the key objectives of which is to extract defective factors of the products.
For example, techniques for defective factor analysis have been disclosed in JP 2005-12095 A including a technique of extracting process data with a large coefficient of correlation by using such analyses as a correlation analysis between the yield and process data linked to each other for every production lot in the manufacturing process and a correlation analysis between product performance and process data etc., and a technique of extracting a variation degree of low-yield lot as a significant difference by linking machines, manufacturing steps and operators processed based on the processing information on the low-yield lot. These techniques are used to identify one specific manufacturing process or manufacturing machine which is a defective factor.
Such analysis of process data is usually performed on all the collected process data sets. However, products may have many defective factors, which may intricately be intertwined with each other. Even if a correlation is present between specific two variates included in a data group, the values of target variates are often changed by the influence of the values of other variates, resulting in no apparent correlation being present therebetween. Therefore, it is usually difficult to extract latent correlations.
Accordingly, in order to help people judge in the above defective factor analysis, the correlation between data sets is usually indicated in graph form on a display screen. There are conventional graph plotting techniques for displaying graphs of all the relations between a plurality of variates belonging to one data group and a plurality of variates belonging to another data group across the screen to capacity or for displaying the correlation between specific two variates by specifying each variate with use of input devices such as keyboards and mouse devices.
The correlations between process data sets on hundreds of steps in the manufacturing process may not be characterized by one element but rather trade-off relations may be present between a plurality of data sets. It is very difficult to efficiently compare a trade-off relation between graphs drawn at the opposite edges of the screen, causing a problem that a lot of analysis time and effort are needed.